International Journal of Artificial Intelligence, Data Science, and Machine Learning
Closed-Loop PBM Output Regulation on Raspberry PI for Safety-Critical Home Devices
Photobiomodulation (PBM) devices employing light- emitting diodes (LEDs) are increasingly adopted for home and clinical applications due to their low cost and compact design [1], [2]. However, many low-cost PBM systems operate in open-loop configurations, resulting in output drift caused by temperature fluctuations, supply voltage variations, and component aging [3], [11]. These deviations can lead to inconsistent optical dosage, raising concerns regarding safety and reproducibility [4], [5]. This paper presents a Raspberry Pi–based closed-loop PBM output regulation system that continuously monitors optical intensity and device temperature, dynamically adjusting the drive current using a proportional-integral (PI) feedback controller [6], [7]. The proposed architecture integrates real-time sensor feedback, adaptive control logic, and software-enforced safety interlocks to maintain stable optical output within predefined bounds [8], [9]. A diffuser-based experimental test rig evaluates system performance under thermal and voltage stress conditions without involving biological testing [11], [12]. Experimental results demonstrate a significant improvement in output stability and response time compared to open-loop operation. This work illustrates how low-cost embedded platforms can implement safety-critical closed-loop regulation for home PBM devices and similar optical systems [10], [13], [14], [15].
Toward Intelligent Enterprise Integration: Cloud-Native Middleware Design Patterns and Adaptive Stream Orchestration Architectures for Autonomous Real-Time Decisioning
The accelerating adoption of cloud-native technologies across enterprise environments has created a compelling imperative to fundamentally reconstitute the middleware layer that governs how distributed services communicate, process events, and coordinate workflows at scale. Conventional integration platforms originally designed for stable, on-premises infrastructure under the assumption of centralized control and synchronous communication are increasingly misaligned with the elastic, ephemeral, and event-driven character of modern enterprise deployments built on containerized microservices, dynamic orchestration engines, and geographically distributed data pipelines. This article presents a unified architectural framework that brings together a formalized catalog of cloud-native middleware design patterns with an adaptive, intelligence-augmented stream orchestration layer capable of supporting autonomous enterprise decision-making in real time. Five structural categories decomposition, event-driven integration, elastic scaling, fault isolation, and declarative deployment form the backbone of the pattern catalog, and layered above this foundation is an adaptive orchestration dimension that pulls artificial intelligence and machine learning directly into the middleware tier, where predictive routing, contextual anomaly detection, and autonomous workflow adaptation become operationally viable rather than theoretically aspirational. Technical implementation strategies, reactive systems design principles, data mesh governance frameworks, and cross-industry deployment scenarios collectively demonstrate that cloud-native, adaptive middleware is not simply a modernization exercise applied to existing integration platforms but a foundational reconstitution of enterprise integration infrastructure commensurate with the velocity, volume, and complexity demands of the contemporary digital enterprise.
Bridging the Enterprise Skills Gap: A Multi-Modal Agentic RAG Architecture for Autonomous Talent Discovery
In the modern enterprise, Human Capital Management (HCM) suffers from a systemic "data-rich, insight-poor" paradox. While platforms like Workday house vast repositories of structured metadata, the most critical indicators of talent niche technical competencies, complex project histories, and latent expertise remain trapped in unstructured formats such as PDF resumes and free-text fields. This paper introduces "Colleague Search," a novel Agentic Multi-Modal Retrieval-Augmented Generation (RAG) framework designed to transform static HR databases into an autonomous discovery engine. By utilizing Azure AI Search, Azure Document Intelligence, and a proprietary "Sidecar Metadata Pattern," this architecture moves beyond simple keyword retrieval to achieve high-fidelity visual document intelligence and semantic reasoning. The implementation effectively solves the "Dark Data" problem inherent in complex resume tables and legacy ingestion pipelines, reducing the talent discovery cycle from 72 hours to sub-second, high-precision identification. Achieving a 98% accuracy rate in skill extraction, this framework serves as a scalable blueprint for autonomous internal mobility, demonstrating how orchestrated AI can fundamentally reshape enterprise resource planning.
Zero Trust Architecture for Smart Factories: Securing Digital Twins and Cyber-Physical OT Systems
The rapid digitization of manufacturing environments has produced highly interconnected cyber-physical ecosystems in which traditional security boundaries no longer hold. Operational Technology (OT) networks once isolated by design now interface with enterprise IT systems, cloud platforms, and AI-driven analytics engines, creating an expanded and heterogeneous attack surface. Perimeter-centric security models, which assume implicit trust within network boundaries, are structurally incapable of addressing this complexity. This paper proposes a comprehensive Zero Trust Architecture (ZTA) tailored for smart factory environments. Five principal contributions are made: (1) an identity-centric trust model anchored in hardware roots of trust and cryptographic device attestation using TPM 2.0 and X.509 certificates, with a PAA-PAI-DAC hierarchy aligned to the CSA Matter specification, and post-quantum cryptographic agility implemented via NIST FIPS 203/204/205; (2) a trust-aware data pipeline that enforces continuous verification at every telemetry stage from field device to cloud analytics and digital twin state; (3) a safety-security co-design framework that reconciles Zero Trust enforcement with the availability and determinism requirements of industrial control systems; (4) a structured threat scenario evaluation anchored to MITRE ATT&CK for ICS techniques T0856, T0859, T0867, and T0862; and (5) a compliance alignment mapping between ZTA principles and IEC 62443 security levels and NIST CSF 2.0 functions. Results demonstrate that identity-centric Zero Trust enforcement significantly reduces the exploitable attack surface while preserving operational continuity in brownfield environments.
Next-Generation Firewall (NGFW) Testing for Gen 6 and Gen 7 Devices
Next-generation firewalls (NGFWs) have to enforce the security policies and detect threats within predominantly encrypted and application traffic, while at the same time ensuring that the user performance at an enterprise scale. This paper presents a rigorous, scalable, and aligned with standards methodology for testing “Gen 6” and “Gen 7” NGFW devices. Building on the IETF’s RFC 9411 benchmarking methodology for the next-generation network security devices, the test objectives are defined, including traffic profiles, Key Performance Indicators (KPIs), testbed architecture, data collection methods, and statistical treatments necessary to evaluate the factors. The factors include security effectiveness, connection scale, throughput, latency, and the cost of TLS/SSL inspection, along with the Deep Packet Inspection (DPI) and the accuracy in the application identification. This framework also assesses the robustness of the devices against the evasion techniques. This paper includes a test matrix, sample reporting templates, and automation guidance to support the results, which can be reviewed.
Predictive Analytics in Asset-Based Finance: Mitigating Credit Risk Using Data-Driven Insights
Asset-based finance (ABF) involves lending against collateral assets, exposing banks to credit risk. Predictive analytics using ML/AI models enables data-driven insights to anticipate defaults and optimize lending decisions. This paper presents a system architecture and predictive workflow for ABF, evaluates model performance using historical and simulated data, and demonstrates improved credit risk mitigation outcomes.
Operational Challenges and Best Practices in MLOps for Enterprise AI Systems
As machine learning systems move from experimental prototypes to production-critical enterprise applications, operational complexity has become one of the biggest barriers to sustained success. While model development has matured rapidly, organizations continue to struggle with deploying, monitoring, governing, and maintaining machine learning systems at scale. These challenges have led to model failures, silent performance degradation, compliance risks, and loss of trust among stakeholders. This paper examines the operational challenges associated with Machine Learning Operations (MLOps) in enterprise environments and presents a set of best practices that address the full AI lifecycle. Rather than focusing on specific tools or platforms, the study emphasizes process design, governance, collaboration, and lifecycle thinking. The paper highlights how enterprises can move from ad-hoc model deployment to reliable, auditable, and scalable AI operations, enabling long-term business value from machine learning investments.
Predictive Failure Detection in Healthcare Integration Middleware Using Hybrid Ensemble Time-Series Machine Learning
Healthcare integration engines process millions of clinical messages daily, yet operational failures including queue saturation, memory exhaustion, thread starvation, and connection pool depletion are detected only after disrupting clinical workflows. This paper presents a Predictive Failure Detection System (PFDS) applying time-series machine learning to integration engine telemetry for proactive failure identification. Three model architectures are evaluated: Long Short-Term Memory (LSTM) networks, Isolation Forest, and a hybrid ensemble combining both with a gradient-boosted meta-classifier. Evaluation across 180 days of simulated enterprise telemetry (200+ channels, 500 messages/second, 847 injected failure events) demonstrates the hybrid ensemble achieves an F1-score of 0.91, median predictive lead-time of 22 minutes, and false positive rate of 4.2%. Detection rates reach 93% for queue saturation and thread starvation, 87–88% for memory exhaustion and connection pool depletion, with the longest observed lead-time at 47 minutes. Aggregate detection of gradual-onset failures (F1–F4) reaches 90.4%. PFDS enables a paradigm shift from reactive incident response to proactive failure prevention in healthcare middleware.
Gamt-Care: A Graph Attention Multimodal Transformer Framework for Crm-Augmented Predictive Healthcare Analytics
The explosion of digital healthcare services has resulted in massive amounts of patient data being collected by hospitals through their CRM systems, including appointment records, follow-ups, and communications with patients about their treatment. Unfortunately, the vast majority of predictive healthcare models today primarily utilize EHR data and largely ignore the behavioral insights that are present in CRM interaction data. To overcome this limitation, this study developed a new intelligent framework for predictive healthcare analytics and AI-enhanced clinical workflow management called GAMT-CARE (Graph Attention Multimodal Transformer for CRM-Augmented Risk Estimation). This framework introduces a new type of patient interaction graph (PIG) that models the various relationships between patients, providers, treatments, and post-treatment communications as they relate to the CRM systems used by hospitals. In order to effectively analyze the heterogeneous data associated with the healthcare ecosystem, a hybrid deep learning architecture known as the GAMTNet (Graph Attention Multimodal Transformer Network) was developed. This model utilizes medical imaging features computed using EfficientNetV2, temporal patient histories modelled using bi-directional long short term memory (Bi-LSTM), and CRM interaction patterns learned through graph neural networks (GNNs) to create a single multimodal representation of each patient's clinical, behavioral, and temporal characteristics that can be fused together using a transformer-based attention mechanism to model the complex relationships among these three different types of features. Additionally, a hybrid swarm intelligence optimization strategy based on the Adaptive Predator Swarm Optimization (APSO), a hybrid bio-inspired optimization algorithm that combines the aggressive swarm behavior of red piranhas (RPO) with the cooperative hunting strategy of grey wolves (GWO) for efficient feature selection and hyperparameter tuning, improves model performance. The suggested approach supports intelligent clinical workflow optimization and individualized healthcare intervention by enabling early prediction of patient risk levels, disease development, and hospital readmission probability.
Enterprise Agentic AI Lifecycle Governance: A Control-Driven Framework from Design to Decommissioning
Agentic Artificial Intelligence (AI) systems, characterized by autonomous multi-step planning and execution capabilities, are increasingly transforming enterprise operations. However, their autonomy introduces novel risks related to security, compliance, and operational control that are not fully addressed by existing AI governance frameworks [1], [4]. This paper proposes a control-driven lifecycle governance framework tailored for agentic AI systems, spanning planning, design, development, deployment, monitoring, and decommissioning. The framework integrates inherent risk assessment, risk tiering, and continuous control validation aligned with established standards such as the NIST AI Risk Management Framework [1]. It further incorporates agent-specific threat modeling approaches, including MITRE ATLAS [2] and MAESTRO [3], to address emerging adversarial risks. A case study demonstrates how the framework enables organizations to operationalize agentic AI systems in a secure, controlled, and compliant manner.
Machine Learning-Powered Fault Detection System for Smart Grid Equipment with Predictive Analytics
As the backbone of modern business and society, a more sustainable and efficient electrical grid is necessary for efficient energy management. Evaluate and predict stability under different conditions since smart grid stabilization is one of the most crucial qualities that might be utilized to assess the effectiveness of smart grid architecture. For smart grid systems to remain stable, operate efficiently, and provide a steady supply of electricity, reliable defect detection is crucial. This study presents a data-driven approach using a neural network (NN) model for fault detection, leveraging the Smart Grid Stability dataset. The proposed NN model is implemented in a Python-based Jupyter Notebook environment and evaluated using standard performance metrics. The results of the experiment show good classification performance, with 98.02% accuracy, 98.92% precision, 98.03% recall, and 98.47% F1-score. Further analyses (confusion matrix, accuracy/loss curves, and ROC curves) support the model's generalizability and resilience. The NN model performs better than the existing state-of-the-art machine learning models (Logistic Regression, Random Forest, and Gradient Boosting) when compared to all important metrics. In general, the results indicate that the suggested NN model provides a trustworthy, saleable, and effective solution for intelligent fault detection in smart grid environments.
A Deep Learning-Based Platform Engineering Framework for Predictive CI/CD Pipeline Optimization and Developer Productivity Enhancement
Continuous Integration and Continuous Deployment (CI/CD) pipelines are foundational to modern software engineering practices, enabling rapid delivery, automation, and quality assurance. However, increasing system complexity, heterogeneous toolchains, and scaling challenges often lead to inefficiencies such as pipeline delays, build failures, resource underutilization, and developer productivity bottlenecks. Traditional rule-based monitoring and reactive optimization techniques are insufficient to address the dynamic and non-linear nature of modern DevOps ecosystems. This paper presents a Deep Learning-Based Platform Engineering Framework designed to enable predictive optimization of CI/CD pipelines while simultaneously enhancing developer productivity. The proposed framework integrates deep neural architectures with platform engineering principles to provide proactive insights, automated decision-making, and adaptive pipeline configurations. By leveraging historical pipeline execution data, system telemetry, and developer interaction patterns, the framework predicts pipeline failures, execution time anomalies, and resource bottlenecks with high accuracy. The architecture consists of four core layers: (1) Data Acquisition Layer, which collects logs, metrics, and developer activity data; (2) Feature Engineering Layer, which transforms raw data into structured representations; (3) Deep Learning Prediction Engine, incorporating models such as LSTM, Transformer networks, and Graph Neural Networks (GNNs) to capture temporal and dependency relationships; and (4) Optimization and Feedback Layer, which dynamically adjusts pipeline parameters and provides actionable recommendations. A key contribution of this work is the integration of platform engineering practices, enabling reusable, standardized CI/CD modules and self-service infrastructure. This reduces cognitive load on developers while ensuring consistency and scalability. The framework also introduces a novel Predictive Pipeline Efficiency Score (PPES), which quantifies pipeline performance based on latency, reliability, and resource utilization. Experimental evaluation demonstrates significant improvements in pipeline efficiency, including reductions in build time, failure rates, and resource consumption. Additionally, developer productivity metrics such as cycle time, deployment frequency, and mean time to recovery (MTTR) show measurable enhancement. The results validate the effectiveness of deep learning in capturing complex pipeline behaviors and enabling proactive optimization strategies. This study contributes to the intersection of DevOps, AI-driven software engineering, and platform engineering by providing a scalable, intelligent framework that bridges operational efficiency and developer experience. The proposed approach has practical implications for organizations seeking to modernize their CI/CD systems and adopt intelligent automation for sustainable software delivery.
Comparative Performance and Techno-Economic Analysis of Wi-Fi 6 and Private 5G Networks in Industrial and Enterprise Environments
The rise of Industry 4.0 has prompted a critical review of wireless connectivity standards, requiring a shift from "best-effort" networking to deterministic, ultra-reliable communication systems. Between 2020 and 2024, two main technologies emerged as contenders for the wireless edge: IEEE 802.11ax, marketed as Wi-Fi 6, and 3GPP Releases 16 and 17, known as Private 5G (P5G) networks. This research paper provides a comprehensive, empirical comparison of these technologies by combining data from academic studies, industry benchmarks, and techno-economic models. The analysis reveals a clear distinction in performance features. While Wi-Fi 6 offers cost-effective solutions for high-density IT environments, measurements show it has unbounded tail latencies reaching up to 264 ms during mobility and packet loss during client roaming. In contrast, Private 5G networks employing scheduled access maintain bounded latencies (typically under 45 ms at the 99.99th percentile) and experience negligible packet loss during handoffs. Moreover, techno-economic models challenge the assumption that Wi-Fi is always cheaper; in large industrial areas (>100,000 sq. ft.), Private 5G can reduce Total Cost of Ownership (TCO) by as much as 53% by lowering infrastructure costs and preventing operational downtime.
The steep rise of artificial intelligence (AI) use in healthcare finance has brought about a major change in risk adjustment and reimbursement mechanisms under Medicare Advantage (MA) programs. AI, based predictive systems are now very commonly used to extract patient records for the assignment of Hierarchical Condition Category (HCC) codes, which in turn results in systematic inflation of risk scores and thus higher government reimbursements. The practice of algorithmic upcoding, as it is often called, constitutes a major risk for the financial viability of public healthcare systems and the Medicare trust fund's ability to continue operating over the long term. In this paper, we suggest an integrated analytical framework that links predictive modelling, healthcare economic analysis, and policy modelling in order to formally define the trade, off between AI predictive accuracy and payment distortion. Through the development of mathematical models, we connect AI, based HCC prediction, risk score calculation, and reimbursement mechanisms to measure the extent of the fiscal distortion effects resulting from algorithmic optimization. The findings reveal the existence of a structural dilemma between the accuracy of the model and the interpretability of the payment; they also show how AI performance, driven optimization can unintentionally increase the economic incentives for systematic upcoding. In addition, we propose a policy, aware simulation framework that helps to assess regulatory measures taken by the Centers for Medicare and Medicaid Services (CMS) under different enforcement and transparency conditions. The study provides a formal foundation for designing regulation-aware AI systems in healthcare finance and proposes a CMS reimbursement reform framework that integrates explainability, auditability, and economic accountability as core design principles. This work represents the first unified AI–economics–policy model for understanding and mitigating algorithmic upcoding in Medicare Advantage systems.
Post-Mortem Intelligence: Using Large Language Models to Build Proactive Reliability Knowledge Graphs from Incident Documentation
Production incident post-mortem documentation represents one of the most underutilized assets in site reliability engineering. Organizations that conduct thorough post-mortem analyses accumulate structured knowledge about failure patterns, root causes, contributing factors, and remediation actions knowledge stored as unstructured text rarely accessed after the specific incident it describes. This paper presents a post-mortem intelligence system that applies Large Language Models to extract structured knowledge from incident documentation and organize it into a searchable reliability knowledge graph, enabling on-call engineers to retrieve historically analogous incidents and their resolution pathways in real time during active investigations. The system was implemented and validated against a corpus of over 300 post-mortem documents accumulated across six credit union banking applications. LLM-based entity extraction using the Claude API identifies services, failure modes, contributing factors, timeline events, and remediation actions from unstructured post-mortem text. Extracted entities and relationships are stored in Amazon Neptune as a directed graph, with semantic similarity search enabling retrieval of analogous historical incidents based on current alert context. Analysis of the accumulated knowledge graph identified 14 systemic anti-patterns recurring across the incident corpus, and proactive infrastructure improvements derived from these patterns are estimated to prevent approximately 6 high-severity incidents annually. This paper presents the extraction methodology, graph schema design, retrieval architecture, and the organizational implications of treating post-mortem documentation as a continuously compounding engineering asset.
Cognitive Load Reduction in On-Call Rotations via Predictive Alert Severity Scoring Using Machine Learning in Financial Cloud Operations
Alert fatigue in cloud-native site reliability engineering represents a systemic threat to both operational reliability and engineer wellbeing. In high-availability financial services environments, on-call engineers receive hundreds of automated alerts per month from integrated observability platforms, of which a substantial fraction are non-actionable noise. This paper presents an ML-driven alert severity scoring framework deployed in production across six credit union banking applications, designed to reduce the cognitive load of on-call engineers by intelligently triaging PagerDuty alert streams before human engagement. The framework trains a gradient-boosted classifier on over 50,000 historical alert events sourced from Dynatrace Davis AI and PagerDuty, engineering 17 features capturing service dependency depth, deployment recency, historical false positive rates, time-of-day patterns, and cross-source metric correlations. Evaluated through a 30-day shadow validation protocol against actual engineer triage decisions, the classifier achieved 89.3% severity classification accuracy with a 2.1% P1 miss rate. Live deployment produced a 34% reduction in actionable alert volume, a 41% reduction in after-hours pages, and a 28% improvement in P1 mean time to acknowledge. A structured engineer wellbeing measurement program documented improvement in post-shift satisfaction scores from 5.8 to 7.4 out of 10 following system activation. This paper presents the feature engineering methodology, the shadow validation protocol, the deployment architecture within a SOC 2 regulated banking environment, and the case for treating engineer wellbeing as a first-class reliability metric.
Pod-Centric Load Balancing: Reducing Command Cancellations in Large-Scale Kubernetes Clusters via Health-Based Node Penalization
In large-scale Kubernetes environments, the default kube-proxy load balanc-ing mechanism often based on random or round-robin distribution fails to ac-count for localized node degradation. This leads to a high frequency of "Command Cancellations” and 5xx errors when requests are routed to pods residing on "grey-failing" nodes. In this research, I propose a Pod-Centric Load Balancing frame-work integrated with Istio Service Mesh. I introduce a novel Health-Based Node Penalization (HBNP) algorithm that dynamically adjusts traffic weights based on real-time node-level telemetry (CPU steal, IO wait, and connection re-sets). My findings demonstrate that by penalizing degraded nodes at the Envoy sidecar level, command cancellations can be reduced by 78% in clusters exceeding 1,000 nodes.
Blockchain-Backed Content Authenticity Verification Framework
One​‍​‌‍​‍‌ of the major challenges of our time is the ability to discern between real and digitally altered content, which has been exacerbated by the rapid rise of digital content, the proliferation of AI-generated media, deepfakes, and very complex identity-spoofing techniques. The traditional ways of verification are having a hard time keeping pace with the speed of falsification; hence, the need for a mechanism that ensures content integrity is massively felt. This article elaborates on a content authenticity verification scheme supported by blockchain technology that aims at re-establishing the trust of the users of the digital information ecosystems by employing the blockchain as a decentralized, tamper-proof trust anchor. The system protects the content by means of metadata hashing, distributed ledger storage, and cryptographic signature validation, which results in easy verification procedures that can detect the changes in the content or verify its legitimate source. Indeed, the framework, through a detailed case study and experimental evaluation, convincingly shows that it keeps excellent performance in ensuring non-repudiation, preventing unauthorized alterations, and enabling traceability in distributed environments. Its potential applications range across the very important domains, such as journalism that needs source verification as a matter of priority; social media platforms that want to fight against false videos and audios; legal and forensic workflows that necessitate digitally verifiable evidence; and enterprise documentation pipelines that depend on audit-ready integrity checks. The proposed solution that brings together blockchain and some lightweight cryptographic measures is a scalable and feasible one that can increase digital trust without the users having to bear a heavy operational overhead. In short, this paper is a significant step towards a more transparent and trustworthy information landscape, as it offers a strong, future-ready infrastructure for the authentication of digital ​‍​‌‍​‍‌content.
LLM-Based Auto-Remediation Model for DevOps Pipeline Failures
DevOps pipelines are the backbone of modern software delivery. They make it very easy to quickly, automatically, and continuously integrate and deploy across many other different cloud environments. However, these pipelines often fail because of configuration drifts, dependency mismatches, infrastructure inconsistencies as well as code-level errors. This causes operational bottlenecks that slow down releases along with their require a lot of human intervention. Standard remediation techniques rely heavily on these pre-written scripts, rigid rule-based frameworks, or engineers doing manual diagnostics. These methods often don't work well with the changing nature of toolchains and multi-cloud architectures. Recent progress in Large Language Models (LLMs) could make these pipelines more reliable by using context-aware reasoning, advanced root-cause analysis, and automated solution formulation. This work presents an LLM-based auto-remediation methodology designed to detect pipeline failures, analyze logs, correlate error patterns, provide corrective actions, and when feasible autonomously rectify issues in actual time. The proposed paradigm integrates natural language understanding with DevOps telemetry, source code analysis & configuration verification to connect detection as well as resolution. Experimental evaluations of widely used these CI/CD systems demonstrate substantial improvements in mean time to recovery (MTTR), a reduction in repetitive manual debugging tasks, and enhanced their pipeline dependability, especially in complex cloud-native workflows. The LLM-driven system is better than typical remediation scripts because it can adapt to the latest types of failures without needing to be reprogrammed, provide better information about the context of mistakes, and works well across teams & environments. This study shows how LLMs can change DevOps operations from being reactive to proactive, cut down on interruptions & let engineers focus on coming up with the latest ideas instead of putting out fires. This is a huge step toward self-healing automation in modern software delivery ecosystems.
Generative AI–Powered Authoring Assistant for Enterprise Content Management
As businesses keep making a lot of documents, like technical manuals, policy guides, customer communications as well as regulatory filings, the need for content that is accurate, consistent & on time has grown. This has made traditional authoring workflows very less effective along with any harder to scale. Manual drafting often requires a lot of back-and-forth effort, deep knowledge of the institution & constant supervision to make sure everything is done very correctly, all of which slow down output. This study introduces a Generative AI-driven Authoring Assistant designed for enterprise content management to address these kinds of challenges. The system uses large language models, specialized datasets, and contextual retrieval mechanisms to automatically create high-quality text, suggest relevant content from existing these repositories, and follow organizational rules like security measures, stylistic guidelines alongside these regulatory standards. Our system includes supervised fine-tuning on anonymized corporate documents, techniques for prompt-engineering to keep the tone consistent & reinforcement signals to improve factual accuracy. There are both qualitative expert evaluations as well as quantitative measures used to assess things like content coherence, compliance, and time-to-completion requirements across different document formats. The results show that authoring time has gone down a lot, that there is more consistency throughout departments, and that there are a lot less compliance corrections during these review cycles. The solution not only boosts productivity, but it also helps firms keep things running smoothly when teams shift by making it easier to reuse their information by showing content in context and reducing the need for specific subject-matter experts. The study focuses on potential directions, such as integrating multimodal content creation, enabling actual time interaction between people and AI, and improving the assistant's ability to navigate intricate regulatory frameworks. The proposed Generative AI-powered Authoring Assistant shows how smart automation could change the way businesses manage their content by combining speed, accuracy as well as organizational coherence in a way that is easy to use and can grow with the business.
Copyright (c) 2026 International Journal of Artificial Intelligence, Data Science, and Machine Learning
AI Builder and Power Automate: Simplifying Business Processes in Dynamics 365
This paper delves into the combination of AI Builder from Microsoft and Power Automate with Dynamics 365 which is revolutionizing business process management through intelligent automation and low-code innovation. The main focus is to rid the world of repetitive, time-consuming workflows by the use of AI-driven insights along with automated actions; thereby, users are free to do higher-value strategic tasks. It considers the extent of automation in different modules of Dynamics 365 that is sales, customer service, and finance, etc. and explains how companies can create intelligent solutions without the need for technical depth. Organizations can utilize AI Builder resources such as form processing, prediction models, and object detection in conjunction with Power Automate’s effortless workflow orchestration to keep the digital transformation fast and at the same time still be agile and compliant. The approach is about creating and testing scenarios for automation in actual situations, accompanied by a case that is sufficient in showing the demonstration of the efficiency, accuracy, and customer satisfaction that have been achieved. The results emphasize that low-code and no-code tools not only make citizen developers capable but also close the gap between business and IT teams; thus, innovation is fostered on a large scale, which is the result. In essence, this integration is a big indication that the intelligent, self-improving ecosystems within Dynamics 365 are the future, where data, AI, and automation are combined to facilitate decision-making, cut down on operational costs, and offer customers personalized experiences. The research work comes to the end with the assertion that the cooperation between Power Automate and AI Builder is a new standard for easy, large-scale, and human-centered process automation in contemporary ​‍​‌‍​‍‌enterprises.
Low-Code Revolution: How Power Platform Extends Dynamics 365 Capabilities
Low-code​‍​‌‍​‍‌ platforms in general and especially Microsoft Power Platform have changed the way new applications are created in large organizations. Power Platform by Microsoft, which is the combination of Power Apps, Power Automate, Power BI, Power Virtual Agents, and Copilot Studio, helps organizations to create, automate, and analyze their business processes with very little coding. The goal of this research is to analyze how these gadgets altogether serve as a bridge between the technical developers and business users, which in turn increases the speed and innovation in enterprise environments. The paper has been descriptive and analytical in nature and has mapped out the major integration scenarios between different modules of Dynamics 365 (for instance, Sales, Finance, and Customer Service) and the elements of Power Platform. Using the examples of the real world, the article communicates that Power Apps allow fast application tailoring, Power Automate manages the workflows by different systems, Power BI converts the operational data into the insights that are actionable, Power Virtual Agents attract customers with the conversational interfaces and Copilot Studio brings in Generative AI for the automation and intelligence. The research shows that such a low-code synergy shortens the time for development, reduces the need for IT support and speeds up digital transformation, while at the same time governance and security are still ensured. The article also talks about the next big changes that are AI copilot usage, citizen development, and easy integration with Azure services as the future of business application evolution. In brief, Microsoft’s Power Platform is not only a handing-over-the-power tool in app development but also it drastically changes the nature of Dynamics 365 from a merely typical ERP-CRM suite into a smart, dynamic ecosystem, which can be referred to as agility-driven. The low-code movement, thus, can be seen as both a strategic enabler and a source of competitive advantage for modern ​‍​‌‍​‍‌businesses.
Data Privacy in the Age of AI: How Dynamics 365 Handles Regulatory Challenges
‍‌As the world is being more and more influenced by artificial intelligence, data privacy has turned into both a central issue for the strategy of a company and a problem for regulation. The paper is about the way that Microsoft Dynamics 365 combines its advanced AI-driven automation with the observance of strict compliance to global data protection frameworks such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA). It looks at how Dynamics 365’s data governance structure, privacy-by-design approach, and security measures based on the cloud contribute to keeping user trust and providing legal accountability in different regions. Microsoft’s approaches for the integration of AI ethics, e.g. the use of data in a responsible way, transparency of the model, and alleviation of bias, are discussed in the study which is in relation to their machine learning and predictive analytics modules. The paper also mentions the platform’s automated compliance reporting and consent management tools which facilitate adherence to the ever-changing regulatory standards.The paper finds that Dynamics 365 manages to keep a balance between smart automations and the maintenance of privacy so that the organizations are allowed to obtain the actionable insights without the rights of the individuals or data integrity being violated. For businesses, the consequences are double: on the one hand, the responsible use of AI as a lever leads to the improvement in operations and the increase of the trust of customers; on the other hand, it makes it possible to establish a measurable model of ethical compliance in the digital economy which can be scaled. This paper is part of the ongoing debate about how AI innovation could be in line with data protection rules. It offers a roadmap for enterprises that have to deal with the intersection of automation, governance, and global ‍​‍‌regulation.
Security and Data Privacy in Redux Stores
In large-scale web applications, Redux has essentially become the main tool for managing state in a predictable way, thus, developers can easily handle complex data flows. On the other hand, with front-end applications handling more and more sensitive data, the question of how to ensure the security and privacy of data stored in Redux has, by far, become the most important issue. This research identifies the different risks that come with improperly managing the state, such as, for example, unauthorized data exposure, session hijacking, and tampering with confidential information. The goal is first and foremost to find out the vulnerabilities in Redux-based architectures and then come up with easy-to-implement privacy-preserving techniques to counteract them.Our plan of action is to combine secure coding practices, encryption of state, authentication layers, and controlled access mechanisms so as to upgrade the resistance of client-side state management to attacks. An in-depth case study shows how these measures can be put into practice in a typical web application, thus, proving that they can be efficient in protecting user data without causing a drop in performance or developer productivity. Results show that despite structural advantages given by Redux in managing state, security issues arise due to its openness, i.e. intentional security design is necessary to avoid data leakage and ensure user trust. The main points of this paper are encryption, token-based authentication, and minimum persistence of sensitive information, thus, the work sets the pace for developers to secure Redux stores in line with contemporary data protection ‌standards.
Dynamic Role Assignment Using Contextual Attributes
In the digital ecosystems that are changing fast, dynamic role assignment is a must for systems to be able to adapt, to be scalable, and to make intelligent decisions such as IoT networks, AI-driven workflows, and modern access control frameworks. Static role models of the past are at a loss with the fluidity of real-time data and changing user contexts and as a result, they are often inefficient, insecure, or slow to respond. This paper presents a contextual attribute–based solution for dynamic role assignment in which user roles are identified and changed automatically based on contextual factors such as location, device type, behavioral patterns, and environmental conditions. The approach put forward combines the analysis of contextual data with the use of rules and machine learning to make sure that roles change as per the requirements without the need for human intervention to the extent that security and compliance are not compromised. The flexibility of the system enables it to be extended to a large and diverse infrastructure without any problems, thus it is easier for the entities to coordinate and the resources to be used more efficiently. Experimental evaluations show that the use of contextual insights allows not only for the accuracy and timeliness of role assignments to be enhanced but also for the overall automation efficiency to be improved. The research uncovers the potential for context-aware mechanisms to revolutionize traditional role management by making it a predictive, self-adjusting process– thus setting the stage for intelligent, responsive digital environments that are able to make a trade-off between flexibility, control, and performance in dynamic ‍​‌‍​‍‌settings.
AOT vs JIT Compilation in Ivy: Benchmarks & Trade-offs
Angular's Ivy rendering engine is a major change of the framework's compilation and rendering paradigm. Essentially, it changes the way the older View Engine works by replacing it with a more efficient, instruction-based architecture that allows locality-aware compilation, better tree shaking, and smaller, more predictable output. In this case, comparing AOT with JIT is very important for understanding how Ivy affects developer experience and production performance. This article compares AOT and JIT to real-world scenarios through benchmarks, which shows that bundle size, build time, startup latency, and runtime are some of the metrics that can be measured. The results indicate that Ivy-powered AOT is always better because it provides smaller bundles and faster load times, thus production deployment is the most favorable scenario. On the other hand, JIT retains its position during development because of fast rebuild cycles and template compilation on-the-fly, however, at the cost of increased runtime. The research breaks down Angular Ivy innovations in architecture to give a clear understanding of the results, showing how the compilation locality and tree-shakable instructions affect the performance trade-offs in different environments. The contributions embody a thorough empirical comparison of AOT and JIT under Angular Ivy, architectural analysis of internal mechanisms leading to differences in performance observable, recommendations for deployment of the engineering teams, and investigation of next steps like better incremental builds and hybrid compilation workflows all aimed at helping determine the best compilation strategy for modern Angular ​‍​‌‍​‍‌applications.
NeuroPerf: Neuromorphic Benchmarking and AI-Optimized Performance Engineering Beyond Von Neumann
Neuromorphic computing, whose operational principles mimic those of the parallel, event-driven human brain, offers a revolutionary change to energy-efficient, context-aware, and dynamically scalable processing models. Unfortunately, as these systems move away from the linear computation paradigms, traditional benchmarking methods are incapable of revealing their performance potential. To address this challenge, NeuroPerf is introduced to be an AI-optimized benchmarking framework for the new era of computation. It moves beyond the conventional performance criteria by considering neural-inspired metrics such as spiking efficiency, synaptic adaptability, and latency-energy correlation parameters that are beyond FLOPS and throughput. NeuroPerf, with AI-driven optimization, understands workload patterns, modifies benchmarks on various neuromorphic processors, and facilitates the comparability of different platforms without strict standardization. This strategy not only leads to rapid innovation in chip design but also makes a close connection between the hardware capabilities and the goals of cognitive computing. NeuroPerf, by integrating benchmarking of the hardware with the attributes of the architectures, creates a living performance ecosystem that next-generation processors can benefit from. To put it simply, it constitutes a paradigm shift from static testing to intelligent performance engineering, thus becoming a foundation for the evaluation and guidance of future processors in a world that is increasingly beyond Von ​‍​‌‍​‍‌Neumann.
ShiftLeft-AI: Machine Learning Framework for Proactive Performance Assurance in CI/CD Pipelines
In the present day's software delivery environments, continuous integration and continuous deployment (CI/CD) pipelines are more essential for speeding up product releases. However, they often run into many performance problems late in the process that lead to costly rollbacks as well as downtime. ShiftLeft-AI is a proactive, machine-learning-based architecture that aims to make sure that their performance is very good from the beginning of the CI/CD process. This is similar to going to the bottom in the development cycle. The suggested method uses these predictive analytics, anomaly detection, along with smart feedback systems to discover their performance issues and system congestion before they are put into use. ShiftLeft-AI can identify many patterns of breakdown & suggest solutions for avoiding them by constantly looking at telemetry information collected during development, tests as well as manufacturing processes. This approach leverages compact machine learning structures directly in the continuous integration pipeline, offering immediate information with no delay, unlike prior reactive monitoring techniques. The system uses adaptive learning and historical information correlation to make these decisions more accurate, cut down on false positives & speed up the search for the core cause. This early-warning system not only lowers the risks of deployment, but it also makes sure that their performance validation becomes an automatic, ongoing & smart process instead of something that has to be done after the release. Experimental results from the prototype implementation show huge improvements in fault detection lead time, less downtime & more confidence in releases. ShiftLeft-AI changes the way DevOps performance management works by combining the flexibility of CI/CD with the predictive power of machine learning. This makes the software delivery ecosystem more reliable, resilient & efficient.
PerfTune360: Self-Optimizing AI Framework for Cloud-Native Microservices
PerfTune360 is a self-optimizing platform powered by these AI that uses continuous learning, adaptive profiling as well as autonomous tuning to make these cloud-native microservices work better. In modern distributed systems, microservices have many problems including configuration drift, different workloads, unpredictable traffic patterns & latency limits that make the system very less stable & the user experience worse. PerfTune360 tackles these problems by adding an artificial neural optimization engine that constantly checks runtime metrics, finds these performance problems, and changes system settings in actual time. The design uses positive reinforcement learning along with predictive modeling to forecast how the workload will behave, making it less difficult to scale up & down while controlling expenditures effectively. PerfTune360 adds lightweight agents to service these meshes, which means there is little extra work to do while still giving you a lot of information about dependencies along with their communication paths. The system has a closed to the outside world feedback mechanism that lets it fix itself. This makes sure that the speed of response is always at its best and the reaction times are continuously consistent, regardless of how the amount of work changes. Tests indicate that PerfTune360 substantially decreases the average reaction time, boosts worker efficiency by making more effective use of resources, and ensures excellent performance in a variety of setting up circumstances. Its modular as well as platform-agnostic structure also lets it interact with popular cloud orchestration applications like Kubernetes, which makes it easy to add existing pipelines rather than halting them from functioning. PerfTune360 marks a shift to self-managing cloud performance, turning traditional manual tuning into an intelligent, self-adapting process that ensures continued efficiency, resilience & scalability for microservice ecosystems of the future generation.
Impact of Interoperability Standards (FHIR HL7) On QA Process
Interoperability remains a major challenge for healthcare information systems networking complex data structures, siloed infrastructures, and non-standardized communication protocols. One of the key drivers toward resolving these issues has been the standardization of communication formats based on Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR). Both of them lay the groundwork for coming to terms with the open, semantically consistent, and client-server-based data exchanges across the clinical domain. QA is a living process that embraces increasingly stringent requirements and rigor of validations at the level of message structures, resource conformance, workflow orchestration, and cross-system compatibility spurred by the coming of these standards. This paper presents an assessment of the effects of HL7 and FHIR on QA practices accomplished by a structured method that industry case analysis, integration testing workflows, and the error-reduction metrics within multi-vendor health IT environments assessment have contributed to. The results show that the use of standard data schemas and REST-based FHIR APIs greatly expands testing coverage by the execution of automated validation scripts, diminishes integration defects by conformance testing driven by schemas, and elevates the reliability of the interoperability tests by the reusable resource profiles and standardized test fixtures. Furthermore, QA teams attain better clarity in terms of regulation as compliance frameworks more and more rely on HL7 and FHIR implementation guides thus easing audit readiness and documentation. The research finds that technology-neutral exchange protocols fuel data exchange not only but also trigger a paradigm shift in QA from manual towards automated, continuous integration and standardized validation pipelines. Later, we will see more extensive employment of FHIR-based test harnesses, AI-assisted conformance checking and lessening of regulatory constraints to aid seamless high-assurance healthcare ‍ ​‍​‌‍​‍‌ecosystems.